Upper Washita River Experimental Watersheds: Data Screening Procedure for Data Quality Assurance

Abstract

The presence of non-stationary conditions in long-term hydrologic observation networks is associated with natural and anthropogenic stressors or network operation problems. Detection and identification of network operation drivers is fundamental in hydrologic investigation due to changes in systematic errors that can exacerbate modeling results or bias research conclusions. We applied a data screening procedure to the USDA-ARS experimental watersheds data sets () in Oklahoma. Detection of statistically significant monotonic trends and changes in mean and variance were used to investigate non-stationary conditions with network operation drivers to assess the impact of changes in the amount of systematic error. Detection of spurious data, filling in missing data, and data screening procedures were applied to >1000 time series, and processed data were made publicly available. The SPELLmap application was used for data processing and statistical tests on watershed segregated data sets and temporally aggregated data. A test for independency (Anderson test), normality, monotonic trend (Spearman test), detection of change point (Pettitt test), and split record test ( and -tests) were used to assess non-stationary conditions. Statistically significant (95% confidence limit) monotonic trends and changes in mean and variance were detected for annual maximum air temperature, rainfall, relative humidity, and solar radiation and in maximum and minimum soil temperature time series. Network operation procedures such as change in calibration protocols and sensor upgrades as well as natural regional weather trends were suspected as driving the detection of statistically significant trends and changes in mean and variance. We concluded that a data screening procedure that identifies changes in systematic errors and detection of false non-stationary conditions in hydrologic problems is fundamental before any modeling applications.

Seasonal Sediment and Nutrient Transport Patterns

“…3) were driven by climate variability, changes in cropped area, and land use and land management. Rainfall measurements at the USDA–ARS Micronet precipitation gauges began in fall 2005 (Guzman et al, 2014). Therefore, no rainfall was plotted in Fig.…”

Upper Washita River Experimental Watersheds: Meteorologic and Soil Climate Measurement Networks

“…Guzman et al (2014) subjected the Level 1 data to a second data screening step (Chu et al, 2014), where the data were evaluated for inconsistencies and nonhomogeneities using long‐term time series analysis. At this time, the second step of the QA methodology is not incorporated by the Oklahoma Climatological Survey into the ARS Micronet QA system that produces products and files.…”

“…At this time, the second step of the QA methodology is not incorporated by the Oklahoma Climatological Survey into the ARS Micronet QA system that produces products and files. However, the data set described and analyzed by Guzman et al (2014) has been subjected to this step and is available for download (for details on how to access this data set, see Guzman et al, 2014). …”

“…This report is designed to complement the data sets described by Starks et al (2014a), Moriasi et al (2014), Zhang et al (2014), Cosh et al (2014), and Guzman et al (2014). Starks et al (2014a) describe nutrient water quality data sets collected in all three research watersheds.…”

“…Cosh et al (2014) examine the temporal stability of the soil moisture measurements in the LWREW and FCREW. Guzman et al (2014) goes into detail on quality control of the meteorologic and soil climate data sets and provides graphical and statistical analyses of these data.…”